AWS DATA ENGINEER VERY VERY URGENT -- 2

AWS DATA ENGINER TRAINER REQUIRED IMMEDIATE BASIS 5 DAYS OF TRAINIG UREGENT BASIS NEED TO START IMMEDAITELY Module 2 - Big Data on AWS Introduction 1. Learning Objective 2. Cloud computing and it's advantages 3. Cloud Computing Models 4. Cloud Service Categories 5. AWS Cloud Platform 6. Design Principles - Part One 7. Design Principles - Part Two 8. [login to view URL] AWS for Big Data - Reasons and Challenges 9. [login to view URL] in AWS 10. Data Warehousing in AWS 11. Redshift, Kinesis and EMR 12. DynamoDB, Machine Learning and Lambda 13. Elastic Search Services and EC2 14. Key Takeaways Module 3 - AWS Big Data Collection Services 1. Learning Objective 2. Amazon Kinesis and Kinesis Stream 3. Kinesis Data Stream Architecture and Core Components 4. Data Producer 5. Data Consumer 6. Kinesis Stream Emitting Data to AWS Services and Kinesis Connector Library 7. Kinesis Firehose 8. Transferring Data Using Lambda 9. Amazon SQS, Lifecycle and Architecture 10. IoT and Big Data 11. IoT Framework 12. AWS Data Pipelines and Data Nodes 13. Activity, Pre-condition and Schedule 14. Key Takeaways Module 4 - AWS Big Data Storage Services 1. Learning Objective 2. Amazon Glacier and Big Data 3. DynamoDB Introduction 4. DynamoDB and EMR 5. DynamoDB Partitions and Distributions 6. DynamoDB GSI LSI 7. DynamoDB Stream and Cross Region Replication 8. DynamoDB Performance and Partition Key Selection 9. Snowball and AWS Big Data 10. AWS DMS 11. AWS Aurora in Big Data 12. Key Takeaways Module 5 - AWS Big Data Processing Services 1. Learning Objective 2. Amazon EMR 3. Apache Hadoop 4. EMR Architecture 5. EMR Releases and Cluster 6. Choosing Instance and Monitoring 7. Demo - Advance EMR Setting Options 8. Hive on EMR 9. HBase with EMR 10. Presto with EMR 11. Spark with EMR 12. EMR File Storage 13. AWS Lambda 14. Key Takeaways Module 6 - Analysis 1. Learning Objective 2. Redshift Intro and Use cases 3. Redshift Architecture 4. MPP and Redshift in AWS Eco-System 5. Columnar Databases 6. Redshift Table Design - Part 2 7. Demo - Redshift Maintenance and Operations 8. Machine Learning Introduction 9. Machine Learning Algorithm 10. Amazon SageMaker 11. Amazon Elasticsearch 12. Amazon Elasticsearch Services 13. Demo - Loading Dataset into Elasticsearch 14. Logstash and R Studio 15. Demo - Fetching the File and Analyzing it using RStudio 16. Athena 17. Demo - Running Query on S3 using the Serverless Athena 18. Key Takeaways Module 7 - Visualization 1. Introduction to Amazon QuickSight 2. Visual Types 3. Story 4. Big Data Visualization 5. Key Takeaways Module 8 - Security